Concavity point and skeleton analysis algorithm for detection and quantization in heavily clumped red blood cells

Izyani Ahmad, Siti Norul Huda Sheikh Abdullah, Raja Zahratul Azma Raja Sabudin

Research output: Contribution to journalArticle

Abstract

In practice, most hospitals use light microscope to examine the smeared blood for blood quantification. This visual quantification is subjective, laborious and time-consuming. Although automating the process is a good solution, the available techniques are unable to count or ignore the clumpy red blood cells (RBC). Moreover, clumping cell can affect the whole counting process of RBC as well as their accuracy. This paper proposes a new quantization process called concavity point and skeleton analysis (CP-SA) for heavily clump RBC. The proposed methodology is based on induction approach, enhanced lime blood cell by using gamma correction to get the appropriate edges. Then, splitting the clump and single cells by calculating each object area in pixel. Later, the quantification of clumpy cells with the proposed CP-SA method is done. This algorithm has been tested on 556 clump RBC taken from thin blood smear images under light microscope. All dataset images are captured from Hematology Unit, UKM Medical Centre in Kuala Lumpur. On all tested images, the cells of interest are successfully detected and counted from those clump cells. A comparative study and analysis to evaluate the performance of the proposed algorithm in three levels of clump have been conducted. The first level was with two clumps, second level with three clumps and third level with four clumps. The counting number of clump cells has been analyzed using quantitative analysis, resulting in much better results compared to other recent algorithms. The comparison shows that the proposed method gives better precision result at all levels with respect to ground truth: two clump cells (92%), three clump cells (96%) and four clump cells (90%). The results prove that this study has successfully developed a new method to count heavily clump RBC more accurately in microscopic images. In addition, this can be considered as a low-cost solution for quantification in massive examination.

Original languageEnglish
Pages (from-to)1900-1906
Number of pages7
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume9
Issue number6
Publication statusPublished - 1 Jan 2019

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Skeleton
skeleton
Blood
erythrocytes
Erythrocytes
Cells
cells
light microscopes
blood
Microscopes
Light
methodology
Hematology
hematology
blood cells
Lime
Blood Cells
quantitative analysis
Cell Count
Pixels

Keywords

  • Concavity Point-Skeleton Analysis (CP-SA)
  • Heavily clump red blood cells quantization
  • Red blood cells
  • Thin blood smear

ASJC Scopus subject areas

  • Computer Science(all)
  • Agricultural and Biological Sciences(all)
  • Engineering(all)

Cite this

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title = "Concavity point and skeleton analysis algorithm for detection and quantization in heavily clumped red blood cells",
abstract = "In practice, most hospitals use light microscope to examine the smeared blood for blood quantification. This visual quantification is subjective, laborious and time-consuming. Although automating the process is a good solution, the available techniques are unable to count or ignore the clumpy red blood cells (RBC). Moreover, clumping cell can affect the whole counting process of RBC as well as their accuracy. This paper proposes a new quantization process called concavity point and skeleton analysis (CP-SA) for heavily clump RBC. The proposed methodology is based on induction approach, enhanced lime blood cell by using gamma correction to get the appropriate edges. Then, splitting the clump and single cells by calculating each object area in pixel. Later, the quantification of clumpy cells with the proposed CP-SA method is done. This algorithm has been tested on 556 clump RBC taken from thin blood smear images under light microscope. All dataset images are captured from Hematology Unit, UKM Medical Centre in Kuala Lumpur. On all tested images, the cells of interest are successfully detected and counted from those clump cells. A comparative study and analysis to evaluate the performance of the proposed algorithm in three levels of clump have been conducted. The first level was with two clumps, second level with three clumps and third level with four clumps. The counting number of clump cells has been analyzed using quantitative analysis, resulting in much better results compared to other recent algorithms. The comparison shows that the proposed method gives better precision result at all levels with respect to ground truth: two clump cells (92{\%}), three clump cells (96{\%}) and four clump cells (90{\%}). The results prove that this study has successfully developed a new method to count heavily clump RBC more accurately in microscopic images. In addition, this can be considered as a low-cost solution for quantification in massive examination.",
keywords = "Concavity Point-Skeleton Analysis (CP-SA), Heavily clump red blood cells quantization, Red blood cells, Thin blood smear",
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year = "2019",
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T1 - Concavity point and skeleton analysis algorithm for detection and quantization in heavily clumped red blood cells

AU - Ahmad, Izyani

AU - Abdullah, Siti Norul Huda Sheikh

AU - Sabudin, Raja Zahratul Azma Raja

PY - 2019/1/1

Y1 - 2019/1/1

N2 - In practice, most hospitals use light microscope to examine the smeared blood for blood quantification. This visual quantification is subjective, laborious and time-consuming. Although automating the process is a good solution, the available techniques are unable to count or ignore the clumpy red blood cells (RBC). Moreover, clumping cell can affect the whole counting process of RBC as well as their accuracy. This paper proposes a new quantization process called concavity point and skeleton analysis (CP-SA) for heavily clump RBC. The proposed methodology is based on induction approach, enhanced lime blood cell by using gamma correction to get the appropriate edges. Then, splitting the clump and single cells by calculating each object area in pixel. Later, the quantification of clumpy cells with the proposed CP-SA method is done. This algorithm has been tested on 556 clump RBC taken from thin blood smear images under light microscope. All dataset images are captured from Hematology Unit, UKM Medical Centre in Kuala Lumpur. On all tested images, the cells of interest are successfully detected and counted from those clump cells. A comparative study and analysis to evaluate the performance of the proposed algorithm in three levels of clump have been conducted. The first level was with two clumps, second level with three clumps and third level with four clumps. The counting number of clump cells has been analyzed using quantitative analysis, resulting in much better results compared to other recent algorithms. The comparison shows that the proposed method gives better precision result at all levels with respect to ground truth: two clump cells (92%), three clump cells (96%) and four clump cells (90%). The results prove that this study has successfully developed a new method to count heavily clump RBC more accurately in microscopic images. In addition, this can be considered as a low-cost solution for quantification in massive examination.

AB - In practice, most hospitals use light microscope to examine the smeared blood for blood quantification. This visual quantification is subjective, laborious and time-consuming. Although automating the process is a good solution, the available techniques are unable to count or ignore the clumpy red blood cells (RBC). Moreover, clumping cell can affect the whole counting process of RBC as well as their accuracy. This paper proposes a new quantization process called concavity point and skeleton analysis (CP-SA) for heavily clump RBC. The proposed methodology is based on induction approach, enhanced lime blood cell by using gamma correction to get the appropriate edges. Then, splitting the clump and single cells by calculating each object area in pixel. Later, the quantification of clumpy cells with the proposed CP-SA method is done. This algorithm has been tested on 556 clump RBC taken from thin blood smear images under light microscope. All dataset images are captured from Hematology Unit, UKM Medical Centre in Kuala Lumpur. On all tested images, the cells of interest are successfully detected and counted from those clump cells. A comparative study and analysis to evaluate the performance of the proposed algorithm in three levels of clump have been conducted. The first level was with two clumps, second level with three clumps and third level with four clumps. The counting number of clump cells has been analyzed using quantitative analysis, resulting in much better results compared to other recent algorithms. The comparison shows that the proposed method gives better precision result at all levels with respect to ground truth: two clump cells (92%), three clump cells (96%) and four clump cells (90%). The results prove that this study has successfully developed a new method to count heavily clump RBC more accurately in microscopic images. In addition, this can be considered as a low-cost solution for quantification in massive examination.

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